EDBT 2026 Demo / reviewers in the wild / expert
Cheng-Zhong Zhang
dblp:182/4796
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis › sequencing data analysis
read filtering |
0.2 | 1 | 2016 | VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016 |
Bioinformatics and computational biology › bioinformatics infrastructure
sequencing data compression |
0.2 | 1 | 2016 | VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016 |
Bioinformatics and computational biology › sequence analysis
sequencing data processing |
0.2 | 1 | 2016 | VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
sequence motif matching · 0.2region-specific filtering rules · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | VariantBam: filtering and profiling of next-generational sequencing data using region-specific rulesabstractUNLABELLED: We developed VariantBam, a C ++ read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis. AVAILABILITY AND IMPLEMENTATION: VariantBam and full documentation are available at github.com/jwalabroad/VariantBam CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jeremiah A. Wala, Cheng-Zhong Zhang, Matthew Meyerson, Rameen Beroukhim |
Bioinform. | 2 |